# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

from __future__ import annotations

from typing import (
    Any,
    Tuple,
)

from pyarrow.interchange.column import (
    DtypeKind,
    ColumnBuffers,
    ColumnNullType,
)

import pyarrow as pa
import re

import pyarrow.compute as pc
from pyarrow.interchange.column import Dtype


# A typing protocol could be added later to let Mypy validate code using
# `from_dataframe` better.
DataFrameObject = Any
ColumnObject = Any
BufferObject = Any


_PYARROW_DTYPES: dict[DtypeKind, dict[int, Any]] = {
    DtypeKind.INT: {8: pa.int8(),
                    16: pa.int16(),
                    32: pa.int32(),
                    64: pa.int64()},
    DtypeKind.UINT: {8: pa.uint8(),
                     16: pa.uint16(),
                     32: pa.uint32(),
                     64: pa.uint64()},
    DtypeKind.FLOAT: {16: pa.float16(),
                      32: pa.float32(),
                      64: pa.float64()},
    DtypeKind.BOOL: {1: pa.bool_(),
                     8: pa.uint8()},
    DtypeKind.STRING: {8: pa.string()},
}


def from_dataframe(df: DataFrameObject, allow_copy=True) -> pa.Table:
    """
    Build a ``pa.Table`` from any DataFrame supporting the interchange protocol.

    Parameters
    ----------
    df : DataFrameObject
        Object supporting the interchange protocol, i.e. `__dataframe__`
        method.
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.Table

    Examples
    --------
    >>> import pyarrow
    >>> from pyarrow.interchange import from_dataframe

    Convert a pandas dataframe to a pyarrow table:

    >>> import pandas as pd
    >>> df = pd.DataFrame({
    ...         "n_attendees": [100, 10, 1],
    ...         "country": ["Italy", "Spain", "Slovenia"],
    ...     })
    >>> df
       n_attendees   country
    0          100     Italy
    1           10     Spain
    2            1  Slovenia
    >>> from_dataframe(df)
    pyarrow.Table
    n_attendees: int64
    country: large_string
    ----
    n_attendees: [[100,10,1]]
    country: [["Italy","Spain","Slovenia"]]
    """
    if isinstance(df, pa.Table):
        return df
    elif isinstance(df, pa.RecordBatch):
        return pa.Table.from_batches([df])

    if not hasattr(df, "__dataframe__"):
        raise ValueError("`df` does not support __dataframe__")

    return _from_dataframe(df.__dataframe__(allow_copy=allow_copy),
                           allow_copy=allow_copy)


def _from_dataframe(df: DataFrameObject, allow_copy=True):
    """
    Build a ``pa.Table`` from the DataFrame interchange object.

    Parameters
    ----------
    df : DataFrameObject
        Object supporting the interchange protocol, i.e. `__dataframe__`
        method.
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.Table
    """
    batches = []
    for chunk in df.get_chunks():
        batch = protocol_df_chunk_to_pyarrow(chunk, allow_copy)
        batches.append(batch)

    if not batches:
        batch = protocol_df_chunk_to_pyarrow(df)
        batches.append(batch)

    return pa.Table.from_batches(batches)


def protocol_df_chunk_to_pyarrow(
    df: DataFrameObject,
    allow_copy: bool = True
) -> pa.RecordBatch:
    """
    Convert interchange protocol chunk to ``pa.RecordBatch``.

    Parameters
    ----------
    df : DataFrameObject
        Object supporting the interchange protocol, i.e. `__dataframe__`
        method.
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.RecordBatch
    """
    # We need a dict of columns here, with each column being a pa.Array
    columns: dict[str, pa.Array] = {}
    for name in df.column_names():
        if not isinstance(name, str):
            raise ValueError(f"Column {name} is not a string")
        if name in columns:
            raise ValueError(f"Column {name} is not unique")
        col = df.get_column_by_name(name)
        dtype = col.dtype[0]
        if dtype in (
            DtypeKind.INT,
            DtypeKind.UINT,
            DtypeKind.FLOAT,
            DtypeKind.STRING,
            DtypeKind.DATETIME,
        ):
            columns[name] = column_to_array(col, allow_copy)
        elif dtype == DtypeKind.BOOL:
            columns[name] = bool_column_to_array(col, allow_copy)
        elif dtype == DtypeKind.CATEGORICAL:
            columns[name] = categorical_column_to_dictionary(col, allow_copy)
        else:
            raise NotImplementedError(f"Data type {dtype} not handled yet")

    return pa.RecordBatch.from_pydict(columns)


def column_to_array(
    col: ColumnObject,
    allow_copy: bool = True,
) -> pa.Array:
    """
    Convert a column holding one of the primitive dtypes to a PyArrow array.
    A primitive type is one of: int, uint, float, bool (1 bit).

    Parameters
    ----------
    col : ColumnObject
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.Array
    """
    buffers = col.get_buffers()
    data_type = col.dtype
    data = buffers_to_array(buffers, data_type,
                            col.size(),
                            col.describe_null,
                            col.offset,
                            allow_copy)
    return data


def bool_column_to_array(
    col: ColumnObject,
    allow_copy: bool = True,
) -> pa.Array:
    """
    Convert a column holding boolean dtype to a PyArrow array.

    Parameters
    ----------
    col : ColumnObject
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.Array
    """
    buffers = col.get_buffers()
    size = buffers["data"][1][1]

    # If booleans are byte-packed a copy to bit-packed will be made
    if size == 8 and not allow_copy:
        raise RuntimeError(
            "Boolean column will be casted from uint8 and a copy "
            "is required which is forbidden by allow_copy=False"
        )

    data_type = col.dtype
    data = buffers_to_array(buffers, data_type,
                            col.size(),
                            col.describe_null,
                            col.offset)
    if size == 8:
        data = pc.cast(data, pa.bool_())

    return data


def categorical_column_to_dictionary(
    col: ColumnObject,
    allow_copy: bool = True,
) -> pa.DictionaryArray:
    """
    Convert a column holding categorical data to a pa.DictionaryArray.

    Parameters
    ----------
    col : ColumnObject
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.DictionaryArray
    """
    if not allow_copy:
        raise RuntimeError(
            "Categorical column will be casted from uint8 and a copy "
            "is required which is forbidden by allow_copy=False"
        )

    categorical = col.describe_categorical

    if not categorical["is_dictionary"]:
        raise NotImplementedError(
            "Non-dictionary categoricals not supported yet")

    # We need to first convert the dictionary column
    cat_column = categorical["categories"]
    dictionary = column_to_array(cat_column)
    # Then we need to convert the indices
    # Here we need to use the buffer data type!
    buffers = col.get_buffers()
    _, data_type = buffers["data"]
    indices = buffers_to_array(buffers, data_type,
                               col.size(),
                               col.describe_null,
                               col.offset)

    # Constructing a pa.DictionaryArray
    dict_array = pa.DictionaryArray.from_arrays(indices, dictionary)

    return dict_array


def parse_datetime_format_str(format_str):
    """Parse datetime `format_str` to interpret the `data`."""

    # timestamp 'ts{unit}:tz'
    timestamp_meta = re.match(r"ts([smun]):(.*)", format_str)
    if timestamp_meta:
        unit, tz = timestamp_meta.group(1), timestamp_meta.group(2)
        if unit != "s":
            # the format string describes only a first letter of the unit, so
            # add one extra letter to convert the unit to numpy-style:
            # 'm' -> 'ms', 'u' -> 'us', 'n' -> 'ns'
            unit += "s"

        return unit, tz

    raise NotImplementedError(f"DateTime kind is not supported: {format_str}")


def map_date_type(data_type):
    """Map column date type to pyarrow date type. """
    kind, bit_width, f_string, _ = data_type

    if kind == DtypeKind.DATETIME:
        unit, tz = parse_datetime_format_str(f_string)
        return pa.timestamp(unit, tz=tz)
    else:
        pa_dtype = _PYARROW_DTYPES.get(kind, {}).get(bit_width, None)

        # Error if dtype is not supported
        if pa_dtype:
            return pa_dtype
        else:
            raise NotImplementedError(
                f"Conversion for {data_type} is not yet supported.")


def buffers_to_array(
    buffers: ColumnBuffers,
    data_type: Tuple[DtypeKind, int, str, str],
    length: int,
    describe_null: ColumnNullType,
    offset: int = 0,
    allow_copy: bool = True,
) -> pa.Array:
    """
    Build a PyArrow array from the passed buffer.

    Parameters
    ----------
    buffer : ColumnBuffers
        Dictionary containing tuples of underlying buffers and
        their associated dtype.
    data_type : Tuple[DtypeKind, int, str, str],
        Dtype description of the column as a tuple ``(kind, bit-width, format string,
        endianness)``.
    length : int
        The number of values in the array.
    describe_null: ColumnNullType
        Null representation the column dtype uses,
        as a tuple ``(kind, value)``
    offset : int, default: 0
        Number of elements to offset from the start of the buffer.
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.Array

    Notes
    -----
    The returned array doesn't own the memory. The caller of this function
    is responsible for keeping the memory owner object alive as long as
    the returned PyArrow array is being used.
    """
    data_buff, _ = buffers["data"]
    try:
        validity_buff, validity_dtype = buffers["validity"]
    except TypeError:
        validity_buff = None
    try:
        offset_buff, offset_dtype = buffers["offsets"]
    except TypeError:
        offset_buff = None

    # Construct a pyarrow Buffer
    data_pa_buffer = pa.foreign_buffer(data_buff.ptr, data_buff.bufsize,
                                       base=data_buff)

    # Construct a validity pyarrow Buffer, if applicable
    if validity_buff:
        validity_pa_buff = validity_buffer_from_mask(validity_buff,
                                                     validity_dtype,
                                                     describe_null,
                                                     length,
                                                     offset,
                                                     allow_copy)
    else:
        validity_pa_buff = validity_buffer_nan_sentinel(data_pa_buffer,
                                                        data_type,
                                                        describe_null,
                                                        length,
                                                        offset,
                                                        allow_copy)

    # Construct a pyarrow Array from buffers
    data_dtype = map_date_type(data_type)

    if offset_buff:
        _, offset_bit_width, _, _ = offset_dtype
        # If an offset buffer exists, construct an offset pyarrow Buffer
        # and add it to the construction of an array
        offset_pa_buffer = pa.foreign_buffer(offset_buff.ptr,
                                             offset_buff.bufsize,
                                             base=offset_buff)

        if data_type[2] == 'U':
            string_type = pa.large_string()
        else:
            if offset_bit_width == 64:
                string_type = pa.large_string()
            else:
                string_type = pa.string()
        array = pa.Array.from_buffers(
            string_type,
            length,
            [validity_pa_buff, offset_pa_buffer, data_pa_buffer],
            offset=offset,
        )
    else:
        array = pa.Array.from_buffers(
            data_dtype,
            length,
            [validity_pa_buff, data_pa_buffer],
            offset=offset,
        )

    return array


def validity_buffer_from_mask(
    validity_buff: BufferObject,
    validity_dtype: Dtype,
    describe_null: ColumnNullType,
    length: int,
    offset: int = 0,
    allow_copy: bool = True,
) -> pa.Buffer:
    """
    Build a PyArrow buffer from the passed mask buffer.

    Parameters
    ----------
    validity_buff : BufferObject
        Tuple of underlying validity buffer and associated dtype.
    validity_dtype : Dtype
        Dtype description as a tuple ``(kind, bit-width, format string,
        endianness)``.
    describe_null : ColumnNullType
        Null representation the column dtype uses,
        as a tuple ``(kind, value)``
    length : int
        The number of values in the array.
    offset : int, default: 0
        Number of elements to offset from the start of the buffer.
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.Buffer
    """
    null_kind, sentinel_val = describe_null
    validity_kind, _, _, _ = validity_dtype
    assert validity_kind == DtypeKind.BOOL

    if null_kind == ColumnNullType.NON_NULLABLE:
        # Sliced array can have a NON_NULLABLE ColumnNullType due
        # to no missing values in that slice of an array though the bitmask
        # exists and validity_buff must be set to None in this case
        return None

    elif null_kind == ColumnNullType.USE_BYTEMASK or (
        null_kind == ColumnNullType.USE_BITMASK and sentinel_val == 1
    ):
        buff = pa.foreign_buffer(validity_buff.ptr,
                                 validity_buff.bufsize,
                                 base=validity_buff)

        if null_kind == ColumnNullType.USE_BYTEMASK:
            if not allow_copy:
                raise RuntimeError(
                    "To create a bitmask a copy of the data is "
                    "required which is forbidden by allow_copy=False"
                )
            mask = pa.Array.from_buffers(pa.int8(), length,
                                         [None, buff],
                                         offset=offset)
            mask_bool = pc.cast(mask, pa.bool_())
        else:
            mask_bool = pa.Array.from_buffers(pa.bool_(), length,
                                              [None, buff],
                                              offset=offset)

        if sentinel_val == 1:
            mask_bool = pc.invert(mask_bool)

        return mask_bool.buffers()[1]

    elif null_kind == ColumnNullType.USE_BITMASK and sentinel_val == 0:
        return pa.foreign_buffer(validity_buff.ptr,
                                 validity_buff.bufsize,
                                 base=validity_buff)
    else:
        raise NotImplementedError(
            f"{describe_null} null representation is not yet supported.")


def validity_buffer_nan_sentinel(
    data_pa_buffer: BufferObject,
    data_type: Dtype,
    describe_null: ColumnNullType,
    length: int,
    offset: int = 0,
    allow_copy: bool = True,
) -> pa.Buffer:
    """
    Build a PyArrow buffer from NaN or sentinel values.

    Parameters
    ----------
    data_pa_buffer : pa.Buffer
        PyArrow buffer for the column data.
    data_type : Dtype
        Dtype description as a tuple ``(kind, bit-width, format string,
        endianness)``.
    describe_null : ColumnNullType
        Null representation the column dtype uses,
        as a tuple ``(kind, value)``
    length : int
        The number of values in the array.
    offset : int, default: 0
        Number of elements to offset from the start of the buffer.
    allow_copy : bool, default: True
        Whether to allow copying the memory to perform the conversion
        (if false then zero-copy approach is requested).

    Returns
    -------
    pa.Buffer
    """
    kind, bit_width, _, _ = data_type
    data_dtype = map_date_type(data_type)
    null_kind, sentinel_val = describe_null

    # Check for float NaN values
    if null_kind == ColumnNullType.USE_NAN:
        if not allow_copy:
            raise RuntimeError(
                "To create a bitmask a copy of the data is "
                "required which is forbidden by allow_copy=False"
            )

        if kind == DtypeKind.FLOAT and bit_width == 16:
            # 'pyarrow.compute.is_nan' kernel not yet implemented
            # for float16
            raise NotImplementedError(
                f"{data_type} with {null_kind} is not yet supported.")
        else:
            pyarrow_data = pa.Array.from_buffers(
                data_dtype,
                length,
                [None, data_pa_buffer],
                offset=offset,
            )
            mask = pc.is_nan(pyarrow_data)
            mask = pc.invert(mask)
            return mask.buffers()[1]

    # Check for sentinel values
    elif null_kind == ColumnNullType.USE_SENTINEL:
        if not allow_copy:
            raise RuntimeError(
                "To create a bitmask a copy of the data is "
                "required which is forbidden by allow_copy=False"
            )

        if kind == DtypeKind.DATETIME:
            sentinel_dtype = pa.int64()
        else:
            sentinel_dtype = data_dtype
        pyarrow_data = pa.Array.from_buffers(sentinel_dtype,
                                             length,
                                             [None, data_pa_buffer],
                                             offset=offset)
        sentinel_arr = pc.equal(pyarrow_data, sentinel_val)
        mask_bool = pc.invert(sentinel_arr)
        return mask_bool.buffers()[1]

    elif null_kind == ColumnNullType.NON_NULLABLE:
        pass
    else:
        raise NotImplementedError(
            f"{describe_null} null representation is not yet supported.")